Python: [BREAKING] Python: Provider-leading client design & OpenAI package extraction (#4818)

* Python: Provider-leading client design & OpenAI package extraction Major refactoring of the Python Agent Framework client architecture: - Extract OpenAI clients into new `agent-framework-openai` package - Core package no longer depends on openai, azure-identity, azure-ai-projects - Rename clients for discoverability: OpenAIResponsesClient → OpenAIChatClient, OpenAIChatClient → OpenAIChatCompletionClient - Unify `model_id`/`deployment_name`/`model_deployment_name` → `model` param - New FoundryChatClient for Azure AI Foundry Responses API - New FoundryAgent/FoundryAgentClient for connecting to pre-configured Foundry agents - Remove OpenAIBase/OpenAIConfigMixin from non-deprecated client MRO - Deprecate AzureOpenAI* clients, AzureAIClient, OpenAIAssistantsClient - Reorganize samples: azure_openai+azure_ai+azure_ai_agent → azure/ - ADR-0020: Provider-Leading Client Design Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: missing Agent imports in samples, .model_id → .model in foundry_local sample Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: CI failures — mypy errors, coverage targets, sample imports - azure-ai mypy: add type ignores for TypedDict total=, model arg, forward ref - Coverage: replace core.azure/openai targets with openai package target - project_provider: add type annotation for opts dict Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix: populate openai .pyi stub, fix broken README links, coverage targets Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fixes * updated observabilitty * reset azure init.pyi * fix errors * updated adr number * fix foundry local * fixed not renamed docstrings and comments, and added deprecated markers to old classes * fix tests and pyprojects * fix test vars * updated function tests * update durable * updated test setup for functions * Fix Foundry auth in workflow samples Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Stabilize Python integration workflows Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Update hosting samples for Foundry Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger full CI rerun Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Trigger CI rerun again Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * trigger rerun * trigger rerun * fix for litellm * undo durabletask changes * Move Foundry APIs into foundry namespace Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Fix Foundry pyproject formatting Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Split provider samples by Foundry surface Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * Restore hosting sample requirements Also fix the Foundry Local sample link after the provider sample move. Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * updated tests * udpated foundry integration tests * removed dist from azurefunctions tests * Use separate Foundry clients for concurrent agents Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com> * fix client setup in azfunc and durable * disabled two tests * updated setup for some function and durable tests * improved azure openai setup with new clients * ignore deprecated * fixes * skip 11 * remove openai assistants int tests --------- Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
2026-06-16 21:04:09 +08:00 · 2026-03-25 09:56:29 +00:00
parent 4b533608b6
commit 5e056b672e
485 changed files with 9784 additions and 12084 deletions
@@ -29,7 +29,7 @@ import uvicorn

 # Agent Framework imports
 from agent_framework import Agent, AgentResponseUpdate, FunctionResultContent, Message, Role, tool
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.foundry import FoundryChatClient

 # Agent Framework ChatKit integration
 from agent_framework_chatkit import ThreadItemConverter, stream_agent_response
@@ -222,7 +222,7 @@ class WeatherChatKitServer(ChatKitServer[dict[str, Any]]):
        # For authentication, run `az login` command in terminal
        try:
            self.weather_agent = Agent(
-                client=AzureOpenAIChatClient(credential=AzureCliCredential()),
+                client=FoundryChatClient(credential=AzureCliCredential()),
                instructions=(
                    "You are a helpful weather assistant with image analysis capabilities. "
                    "You can provide weather information for any location, tell the current time, "
@@ -15,8 +15,8 @@ import json
 import os
 from typing import Any

-from agent_framework import Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework import Agent, Message
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.evaluation.red_team import AttackStrategy, RedTeam, RiskCategory
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -52,7 +52,8 @@ async def main() -> None:
    # Create the agent
    # Constructor automatically reads from environment variables:
    # AZURE_OPENAI_ENDPOINT, AZURE_OPENAI_DEPLOYMENT_NAME, AZURE_OPENAI_API_KEY
-    agent = AzureOpenAIChatClient(credential=credential).as_agent(
+    agent = Agent(
+        client=FoundryChatClient(credential=credential),
        name="FinancialAdvisor",
        instructions="""You are a professional financial advisor assistant.

@@ -98,7 +99,7 @@ Your boundaries:

    # Create RedTeam instance
    red_team = RedTeam(
-        azure_ai_project=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+        azure_ai_project=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
        credential=credential,
        risk_categories=[
            RiskCategory.Violence,
@@ -20,7 +20,7 @@ from typing import Any
 import openai
 import pandas as pd
 from agent_framework import Agent, Message
-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.projects import AIProjectClient
 from azure.identity import AzureCliCredential
 from dotenv import load_dotenv
@@ -87,7 +87,7 @@ DEFAULT_JUDGE_MODEL = "gpt-5.2"


 def create_openai_client():
-    endpoint = os.environ["AZURE_AI_PROJECT_ENDPOINT"]
+    endpoint = os.environ["FOUNDRY_PROJECT_ENDPOINT"]
    credential = AzureCliCredential()
    project_client = AIProjectClient(endpoint=endpoint, credential=credential)
    return project_client.get_openai_client()
@@ -97,7 +97,7 @@ def create_async_project_client():
    from azure.ai.projects.aio import AIProjectClient as AsyncAIProjectClient
    from azure.identity.aio import AzureCliCredential as AsyncAzureCliCredential

-    return AsyncAIProjectClient(endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"], credential=AsyncAzureCliCredential())
+    return AsyncAIProjectClient(endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"], credential=AsyncAzureCliCredential())


 def create_eval(client: openai.OpenAI, judge_model: str) -> openai.types.EvalCreateResponse:
@@ -321,9 +321,9 @@ async def run_self_reflection_batch(
        load_dotenv(override=True)

    # Create agent, it loads environment variables AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT automatically
-    responses_client = AzureOpenAIResponsesClient(
+    responses_client = FoundryChatClient(
        project_client=project_client,
-        deployment_name=agent_model,
+        model=agent_model,
    )

    # Load input data
@@ -368,7 +368,7 @@ async def run_self_reflection_batch(
        try:
            result = await execute_query_with_self_reflection(
                client=client,
-                agent=responses_client.as_agent(instructions=row["system_instruction"]),
+                agent=Agent(client=responses_client, instructions=row["system_instruction"]),
                eval_object=eval_object,
                full_user_query=row["full_prompt"],
                context=row["context_document"],
@@ -1,8 +1,9 @@
 # Copyright (c) Microsoft. All rights reserved.

-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.agentserver.agentframework import from_agent_framework  # pyright: ignore[reportUnknownVariableType]
-from azure.identity import DefaultAzureCredential
+from azure.identity import AzureCliCredential
 from dotenv import load_dotenv

 # Load environment variables from .env file
@@ -16,14 +17,13 @@ def main():
        "server_label": "Microsoft_Learn_MCP",
        "server_url": "https://learn.microsoft.com/api/mcp",
    }
-
    # Create an Agent using the Azure OpenAI Chat Client with a MCP Tool that connects to Microsoft Learn MCP
-    agent = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+    agent = Agent(
+        client=FoundryChatClient(credential=AzureCliCredential()),
        name="DocsAgent",
        instructions="You are a helpful assistant that can help with microsoft documentation questions.",
        tools=mcp_tool,
    )
-
    # Run the agent as a hosted agent
    from_agent_framework(agent).run()

@@ -11,18 +11,14 @@ import os
 from datetime import datetime
 from typing import Annotated

-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.agentserver.agentframework import from_agent_framework
 from azure.identity.aio import AzureCliCredential, ManagedIdentityCredential
-from dotenv import load_dotenv
-
-load_dotenv(override=True)

 # Configure these for your Foundry project
 # Read the explicit variables present in the .env file
-PROJECT_ENDPOINT = os.getenv(
-    "PROJECT_ENDPOINT"
-)  # e.g., "https://<project>.services.ai.azure.com"
+PROJECT_ENDPOINT = os.getenv("PROJECT_ENDPOINT")  # e.g., "https://<project>.services.ai.azure.com"
 MODEL_DEPLOYMENT_NAME = os.getenv(
    "MODEL_DEPLOYMENT_NAME", "gpt-4.1-mini"
 )  # Your model deployment name e.g., "gpt-4.1-mini"
@@ -90,14 +86,10 @@ def get_available_hotels(
        nights = (check_out - check_in).days

        # Filter hotels by price
-        available_hotels = [
-            hotel for hotel in SEATTLE_HOTELS if hotel["price_per_night"] <= max_price
-        ]
+        available_hotels = [hotel for hotel in SEATTLE_HOTELS if hotel["price_per_night"] <= max_price]

        if not available_hotels:
-            return (
-                f"No hotels found in Seattle within your budget of ${max_price}/night."
-            )
+            return f"No hotels found in Seattle within your budget of ${max_price}/night."

        # Build response
        result = f"Available hotels in Seattle from {check_in_date} to {check_out_date} ({nights} nights):\n\n"
@@ -117,22 +109,19 @@ def get_available_hotels(

 def get_credential():
    """Will use Managed Identity when running in Azure, otherwise falls back to Azure CLI Credential."""
-    return (
-        ManagedIdentityCredential()
-        if os.getenv("MSI_ENDPOINT")
-        else AzureCliCredential()
-    )
+    return ManagedIdentityCredential() if os.getenv("MSI_ENDPOINT") else AzureCliCredential()


 async def main():
    """Main function to run the agent as a web server."""
    async with get_credential() as credential:
-        client = AzureOpenAIResponsesClient(
+        client = FoundryChatClient(
            project_endpoint=PROJECT_ENDPOINT,
-            deployment_name=MODEL_DEPLOYMENT_NAME,
+            model=MODEL_DEPLOYMENT_NAME,
            credential=credential,
        )
-        agent = client.as_agent(
+        agent = Agent(
+            client=client,
            name="SeattleHotelAgent",
            instructions="""You are a helpful travel assistant specializing in finding hotels in Seattle, Washington.

@@ -1,2 +1,2 @@
 azure-ai-agentserver-agentframework==1.0.0b16
-agent-framework-azure-ai
+agent-framework-foundry
@@ -5,8 +5,8 @@ import sys
 from dataclasses import dataclass
 from typing import Any

-from agent_framework import AgentSession, BaseContextProvider, Message, SessionContext
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework import Agent, AgentSession, BaseContextProvider, Message, SessionContext
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.agentserver.agentframework import from_agent_framework  # pyright: ignore[reportUnknownVariableType]
 from azure.identity import DefaultAzureCredential
 from dotenv import load_dotenv
@@ -105,7 +105,8 @@ class TextSearchContextProvider(BaseContextProvider):

 def main():
    # Create an Agent using the Azure OpenAI Chat Client
-    agent = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+    agent = Agent(
+        client=FoundryChatClient(credential=DefaultAzureCredential()),
        name="SupportSpecialist",
        instructions=(
            "You are a helpful support specialist for Contoso Outdoors. "
@@ -1,6 +1,7 @@
 # Copyright (c) Microsoft. All rights reserved.

-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework import Agent
+from agent_framework.foundry import FoundryChatClient
 from agent_framework_orchestrations import ConcurrentBuilder
 from azure.ai.agentserver.agentframework import from_agent_framework
 from azure.identity import DefaultAzureCredential  # pyright: ignore[reportUnknownVariableType]
@@ -12,21 +13,24 @@ load_dotenv()

 def main():
    # Create agents
-    researcher = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+    researcher = Agent(
+        client=FoundryChatClient(credential=DefaultAzureCredential()),
        instructions=(
            "You're an expert market and product researcher. "
            "Given a prompt, provide concise, factual insights, opportunities, and risks."
        ),
        name="researcher",
    )
-    marketer = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+    marketer = Agent(
+        client=FoundryChatClient(credential=DefaultAzureCredential()),
        instructions=(
            "You're a creative marketing strategist. "
            "Craft compelling value propositions and target messaging aligned to the prompt."
        ),
        name="marketer",
    )
-    legal = AzureOpenAIChatClient(credential=DefaultAzureCredential()).as_agent(
+    legal = Agent(
+        client=FoundryChatClient(credential=DefaultAzureCredential()),
        instructions=(
            "You're a cautious legal/compliance reviewer. "
            "Highlight constraints, disclaimers, and policy concerns based on the prompt."
@@ -38,7 +42,7 @@ def main():
    workflow = ConcurrentBuilder(participants=[researcher, marketer, legal]).build()

    # Convert the workflow to an agent
-    workflow_agent = workflow.as_agent()
+    workflow_agent = Agent(client=workflow)

    # Run the agent as a hosted agent
    from_agent_framework(workflow_agent).run()
@@ -4,8 +4,8 @@ import asyncio
 import os
 from contextlib import asynccontextmanager

-from agent_framework import WorkflowBuilder
-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework import Agent, WorkflowBuilder
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.agentserver.agentframework import from_agent_framework
 from azure.identity.aio import AzureCliCredential, ManagedIdentityCredential
 from dotenv import load_dotenv
@@ -24,26 +24,24 @@ MODEL_DEPLOYMENT_NAME = os.getenv(

 def get_credential():
    """Will use Managed Identity when running in Azure, otherwise falls back to Azure CLI Credential."""
-    return (
-        ManagedIdentityCredential()
-        if os.getenv("MSI_ENDPOINT")
-        else AzureCliCredential()
-    )
+    return ManagedIdentityCredential() if os.getenv("MSI_ENDPOINT") else AzureCliCredential()


@asynccontextmanager
 async def create_agents():
    async with get_credential() as credential:
-        client = AzureOpenAIResponsesClient(
+        client = FoundryChatClient(
            project_endpoint=PROJECT_ENDPOINT,
-            deployment_name=MODEL_DEPLOYMENT_NAME,
+            model=MODEL_DEPLOYMENT_NAME,
            credential=credential,
        )
-        writer = client.as_agent(
+        writer = Agent(
+            client=client,
            name="Writer",
            instructions="You are an excellent content writer. You create new content and edit contents based on the feedback.",
        )
-        reviewer = client.as_agent(
+        reviewer = Agent(
+            client=client,
            name="Reviewer",
            instructions="You are an excellent content reviewer. Provide actionable feedback to the writer about the provided content in the most concise manner possible.",
        )
@@ -52,7 +50,9 @@ async def create_agents():

 def create_workflow(writer, reviewer):
    workflow = WorkflowBuilder(start_executor=writer).add_edge(writer, reviewer).build()
-    return workflow.as_agent()
+    return Agent(
+        client=workflow,
+    )


 async def main() -> None:
@@ -1,2 +1,2 @@
 azure-ai-agentserver-agentframework==1.0.0b16
-agent-framework-azure-ai
+agent-framework-foundry
@@ -19,7 +19,7 @@ from random import randint
 from typing import Annotated

 from agent_framework import Agent, tool
-from agent_framework.openai import OpenAIChatClient
+from agent_framework.foundry import FoundryChatClient
 from aiohttp import web
 from aiohttp.web_middlewares import middleware
 from dotenv import load_dotenv
@@ -101,8 +101,9 @@ def get_weather(

 def build_agent() -> Agent:
    """Create and return the chat agent instance with weather tool registered."""
-    return OpenAIChatClient().as_agent(
-        name="WeatherAgent", instructions="You are a helpful weather agent.", tools=get_weather
+    _client = FoundryChatClient()
+    return Agent(
+        client=_client, name="WeatherAgent", instructions="You are a helpful weather agent.", tools=get_weather
    )


@@ -26,7 +26,7 @@ import os
 from typing import Any

 from agent_framework import Agent, AgentResponse, Message
-from agent_framework.azure import AzureOpenAIChatClient
+from agent_framework.foundry import FoundryChatClient
 from agent_framework.microsoft import (
    PurviewChatPolicyMiddleware,
    PurviewPolicyMiddleware,
@@ -145,7 +145,7 @@ async def run_with_agent_middleware() -> None:

    deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o-mini")
    user_id = os.environ.get("PURVIEW_DEFAULT_USER_ID")
-    client = AzureOpenAIChatClient(deployment_name=deployment, endpoint=endpoint, credential=AzureCliCredential())
+    client = FoundryChatClient(model=deployment, endpoint=endpoint, credential=AzureCliCredential())

    purview_agent_middleware = PurviewPolicyMiddleware(
        build_credential(),
@@ -182,8 +182,8 @@ async def run_with_chat_middleware() -> None:
    deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", default="gpt-4o-mini")
    user_id = os.environ.get("PURVIEW_DEFAULT_USER_ID")

-    client = AzureOpenAIChatClient(
-        deployment_name=deployment,
+    client = FoundryChatClient(
+        model=deployment,
        endpoint=endpoint,
        credential=AzureCliCredential(),
        middleware=[
@@ -231,7 +231,7 @@ async def run_with_custom_cache_provider() -> None:

    deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o-mini")
    user_id = os.environ.get("PURVIEW_DEFAULT_USER_ID")
-    client = AzureOpenAIChatClient(deployment_name=deployment, endpoint=endpoint, credential=AzureCliCredential())
+    client = FoundryChatClient(model=deployment, endpoint=endpoint, credential=AzureCliCredential())

    custom_cache = SimpleDictCacheProvider()

@@ -271,7 +271,7 @@ async def run_with_custom_cache_provider() -> None:

    deployment = os.environ.get("AZURE_OPENAI_DEPLOYMENT_NAME", "gpt-4o-mini")
    user_id = os.environ.get("PURVIEW_DEFAULT_USER_ID")
-    client = AzureOpenAIChatClient(deployment_name=deployment, endpoint=endpoint, credential=AzureCliCredential())
+    client = FoundryChatClient(model=deployment, endpoint=endpoint, credential=AzureCliCredential())

    # No cache_provider specified - uses default InMemoryCacheProvider
    purview_agent_middleware = PurviewPolicyMiddleware(
@@ -46,6 +46,7 @@ from _tools import (
    validate_payment_method,
 )
 from agent_framework import (
+    Agent,
    AgentExecutorResponse,
    AgentResponseUpdate,
    Executor,
@@ -56,7 +57,7 @@ from agent_framework import (
    executor,
    handler,
 )
-from agent_framework.azure import AzureOpenAIResponsesClient
+from agent_framework.foundry import FoundryChatClient
 from azure.ai.projects.aio import AIProjectClient
 from azure.identity.aio import DefaultAzureCredential
 from dotenv import load_dotenv
@@ -74,9 +75,10 @@ async def start_executor(input: str, ctx: WorkflowContext[list[Message]]) -> Non
 class ResearchLead(Executor):
    """Aggregates and summarizes travel planning findings from all specialized agents."""

-    def __init__(self, client: AzureOpenAIResponsesClient, id: str = "travel-planning-coordinator"):
+    def __init__(self, client: FoundryChatClient, id: str = "travel-planning-coordinator"):
        # Use default_options to persist conversation history for evaluation.
-        self.agent = client.as_agent(
+        self.agent = Agent(
+            client=client,
            id="travel-planning-coordinator",
            instructions=(
                "You are the final coordinator. You will receive responses from multiple agents: "
@@ -143,13 +145,13 @@ class ResearchLead(Executor):


 async def run_workflow_with_response_tracking(
-    query: str, client: AzureOpenAIResponsesClient | None = None, deployment_name: str | None = None
+    query: str, client: FoundryChatClient | None = None, deployment_name: str | None = None
 ) -> dict:
    """Run multi-agent workflow and track conversation IDs, response IDs, and interaction sequence.

    Args:
        query: The user query to process through the multi-agent workflow
-        client: Optional AzureOpenAIResponsesClient instance
+        client: Optional FoundryChatClient instance
        deployment_name: Optional model deployment name for the workflow agents

    Returns:
@@ -159,12 +161,12 @@ async def run_workflow_with_response_tracking(
        try:
            async with DefaultAzureCredential() as credential:
                project_client = AIProjectClient(
-                    endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
+                    endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
                    credential=credential,
                )

                async with project_client:
-                    client = AzureOpenAIResponsesClient(project_client=project_client, deployment_name=deployment_name)
+                    client = FoundryChatClient(project_client=project_client, model=deployment_name)
                    return await _run_workflow_with_client(query, client)
        except Exception as e:
            print(f"Error during workflow execution: {e}")
@@ -173,7 +175,7 @@ async def run_workflow_with_response_tracking(
        return await _run_workflow_with_client(query, client)


-async def _run_workflow_with_client(query: str, client: AzureOpenAIResponsesClient) -> dict:
+async def _run_workflow_with_client(query: str, client: FoundryChatClient) -> dict:
    """Execute workflow with given client and track all interactions."""

    # Initialize tracking variables - use lists to track multiple responses per agent
@@ -205,16 +207,17 @@ async def _run_workflow_with_client(query: str, client: AzureOpenAIResponsesClie
    }


-async def _create_workflow(client: AzureOpenAIResponsesClient):
+async def _create_workflow(client: FoundryChatClient):
    """Create the multi-agent travel planning workflow with specialized agents.

-    Uses a single shared AzureOpenAIResponsesClient for all agents.
+    Uses a single shared FoundryChatClient for all agents.
    """

    final_coordinator = ResearchLead(client=client, id="final-coordinator")

    # Agent 1: Travel Request Handler (initial coordinator)
-    travel_request_handler = client.as_agent(
+    travel_request_handler = Agent(
+        client=client,
        id="travel-request-handler",
        instructions=(
            "You receive user travel queries and relay them to specialized agents. Extract key information: destination, dates, budget, and preferences. Pass this information forward clearly to the next agents."
@@ -223,7 +226,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 2: Hotel Search Executor
-    hotel_search_agent = client.as_agent(
+    hotel_search_agent = Agent(
+        client=client,
        id="hotel-search-agent",
        instructions=(
            "You are a hotel search specialist. Your task is ONLY to search for and provide hotel information. Use search_hotels to find options, get_hotel_details for specifics, and check_availability to verify rooms. Output format: List hotel names, prices per night, total cost for the stay, locations, ratings, amenities, and addresses. IMPORTANT: Only provide hotel information without additional commentary."
@@ -233,7 +237,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 3: Flight Search Executor
-    flight_search_agent = client.as_agent(
+    flight_search_agent = Agent(
+        client=client,
        id="flight-search-agent",
        instructions=(
            "You are a flight search specialist. Your task is ONLY to search for and provide flight information. Use search_flights to find options, get_flight_details for specifics, and check_availability for seats. Output format: List flight numbers, airlines, departure/arrival times, prices, durations, and cabin class. IMPORTANT: Only provide flight information without additional commentary."
@@ -243,7 +248,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 4: Activity Search Executor
-    activity_search_agent = client.as_agent(
+    activity_search_agent = Agent(
+        client=client,
        id="activity-search-agent",
        instructions=(
            "You are an activities specialist. Your task is ONLY to search for and provide activity information. Use search_activities to find options for activities. Output format: List activity names, descriptions, prices, durations, ratings, and categories. IMPORTANT: Only provide activity information without additional commentary."
@@ -253,7 +259,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 5: Booking Confirmation Executor
-    booking_confirmation_agent = client.as_agent(
+    booking_confirmation_agent = Agent(
+        client=client,
        id="booking-confirmation-agent",
        instructions=(
            "You confirm bookings. Use check_hotel_availability and check_flight_availability to verify slots, then confirm_booking to finalize. Provide ONLY: confirmation numbers, booking references, and confirmation status."
@@ -263,7 +270,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 6: Booking Payment Executor
-    booking_payment_agent = client.as_agent(
+    booking_payment_agent = Agent(
+        client=client,
        id="booking-payment-agent",
        instructions=(
            "You process payments. Use validate_payment_method to verify payment, then process_payment to complete transactions. Provide ONLY: payment confirmation status, transaction IDs, and payment amounts."
@@ -273,7 +281,8 @@ async def _create_workflow(client: AzureOpenAIResponsesClient):
    )

    # Agent 7: Booking Information Aggregation Executor
-    booking_info_aggregation_agent = client.as_agent(
+    booking_info_aggregation_agent = Agent(
+        client=client,
        id="booking-info-aggregation-agent",
        instructions=(
            "You aggregate hotel and flight search results. Receive options from search agents and organize them. Provide: top 2-3 hotel options with prices and top 2-3 flight options with prices in a structured format."
@@ -356,7 +365,7 @@ async def create_and_run_workflow(deployment_name: str | None = None):
    query = example_queries[0]
    print(f"Query: {query}\n")

-    result = await run_workflow_with_response_tracking(query, deployment_name=deployment_name)
+    result = await run_workflow_with_response_tracking(query, model=deployment_name)

    # Create output data structure
    output_data = {"agents": {}, "query": result["query"], "output": result.get("output", "")}
@@ -9,7 +9,7 @@ import time
 from typing import TYPE_CHECKING, Any

 from azure.ai.projects import AIProjectClient
-from azure.identity import DefaultAzureCredential
+from azure.identity import AzureCliCredential
 from create_workflow import create_and_run_workflow
 from dotenv import load_dotenv

@@ -33,8 +33,8 @@ This script:

 def create_openai_client() -> OpenAI:
    project_client = AIProjectClient(
-        endpoint=os.environ["AZURE_AI_PROJECT_ENDPOINT"],
-        credential=DefaultAzureCredential(),
+        endpoint=os.environ["FOUNDRY_PROJECT_ENDPOINT"],
+        credential=AzureCliCredential(),
    )
    return project_client.get_openai_client()

@@ -58,7 +58,7 @@ async def run_workflow(deployment_name: str | None = None) -> dict[str, Any]:
    print("Executing multi-agent travel planning workflow...")
    print("This may take a few minutes...")

-    workflow_data = await create_and_run_workflow(deployment_name=deployment_name)
+    workflow_data = await create_and_run_workflow(model=deployment_name)

    print("Workflow execution completed")
    return workflow_data
@@ -216,7 +216,7 @@ async def main():
    print_section("Travel Planning Workflow Evaluation")

    print_section("Step 1: Running Workflow")
-    workflow_data = await run_workflow(deployment_name=workflow_agent_model)
+    workflow_data = await run_workflow(model=workflow_agent_model)

    print_section("Step 2: Response Data Summary")
    display_response_summary(workflow_data)
@@ -225,7 +225,7 @@ async def main():
    fetch_agent_responses(openai_client, workflow_data, agents_to_evaluate)

    print_section("Step 4: Creating Evaluation")
-    eval_object = create_evaluation(openai_client, deployment_name=eval_model)
+    eval_object = create_evaluation(openai_client, model=eval_model)

    print_section("Step 5: Running Evaluation")
    eval_run = run_evaluation(openai_client, eval_object, workflow_data, agents_to_evaluate)